Explore the vast range of titles available.

Ce manuel permet de comprendre comment appliquer des méthodes ethnographiques et ethnologiques dans l'observation sociale spécifique à l'intervention sociale. L'auteur invite le lecteur à entrer directement dans des exemples concrets d'observation sociale, comme si l'observation sociale était en train de se faire, de se produire. Le lecteur acquiert ainsi des techniques pour étudier la réalité d'un quartier, d'une ville ou d'un groupe de personnes. Cet ouvrage peut guider les équipes qui interviennent sur le terrain pour investir des méthodes concrètes et inciter les cadres de direction à construire des protocoles d'action.

Dans mon récit, j’ai contracté une expérience d’enseignement du karaté d’une dizaine d’années. Cette aventure s’est vécue dans un dojo de fortune, planté au cœur d’un quartier populaire d’une petite ville française d’une agglomération de plus de 100 000 habitants. Les jeunes qui étaient bénéficiaires de mon enseignement se distinguaient par leurs difficultés sociales ou familiales. Pour faire vivre l’esprit du cadre dans lequel j’installais la pratique martiale, j’ai fait le choix de narrer en amont de cette expérience les motivations qui étaient les miennes et les raisons qui me poussaient à cette mise en œuvre. Ce détour est fondamental:

Aim Processes and variables measured in ecology are almost always spatially autocorrelated, potentially leading to the choice of overly complex models when performing variable selection. One way to solve this problem is to account for residual spatial autocorrelation (RSA) for each subset of variables considered and then use a classical model selection criterion such as the Akaike information criterion (AIC). However, this method can be laborious and it raises other concerns such as which spatial model to use or how to compare different spatial models. To improve the accuracy of variable selection in ecology, this study evaluates an alternative method based on a spatial cross-validation procedure. Such a procedure is usually used for model evaluation but can also provide interesting outcomes for variable selection in the presence of spatial autocorrelation. Innovation We propose to use a special case of spatial cross-validation, spatial leave-one-out (SLOO), giving a criterion equivalent to the AIC in the absence of spatial autocorrelation. SLOO only computes non-spatial models and uses a threshold distance (equal to the range of RSA) to keep each point left out spatially independent from the others. We first provide some simulations to evaluate how SLOO performs compared with AIC.We then assess the robustness of SLOO on a large-scale dataset. R software codes are provided for generalized linear models. Main conclusions The AIC was relevant for variable selection in the presence of RSA if the independent variables considered were not spatially autocorrelated. It otherwise failed because highly spatially autocorrelated variables were more often selected than others. Conversely, SLOO had similar performances whether the variables were themselves spatially autocorrelated or not. It was particularly useful when the range of RSA was small, which is a common property of spatial tools. SLOO appears to be a promising solution for selecting relevant variables from most ecological spatial datasets.

Purpose The aim of this retrospective study was to determine if some features of baseline 18 F-FDG PET images, including volume and heterogeneity, reflect clinical, histological or immunohistochemical characteristics in patients with stage II or III breast cancer (BC). Methods Included in the present retrospective analysis were 171 prospectively recruited patients with stage II/III BC treated consecutively at Saint-Louis hospital. Primary tumour volumes were semiautomatically delineated on pretreatment 18 F-FDG PET images. The parameters extracted included SUV max , SUV mean , metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and heterogeneity quantified using the area under the curve of the cumulative histogram and textural features. Associations between clinical/histopathological characteristics and 18 F-FDG PET features were assessed using one-way analysis of variance. Areas under the ROC curves (AUC) were used to quantify the discriminative power of the features significantly associated with clinical/histopathological characteristics. Results T3 tumours (>5 cm) exhibited higher textural heterogeneity in 18 F-FDG uptake than T2 tumours (AUC <0.75), whereas there were no significant differences in SUV max and SUV mean . Invasive ductal carcinoma showed higher SUV max values than invasive lobular carcinoma ( p  = 0.008) but MATV, TLG and textural features were not discriminative. Grade 3 tumours had higher FDG uptake (AUC 0.779 for SUV max and 0.694 for TLG), and exhibited slightly higher regional heterogeneity (AUC 0.624). Hormone receptor-negative tumours had higher SUV values than oestrogen receptor-positive (ER-positive) and progesterone receptor-positive tumours, while heterogeneity patterns showed only low-level variation according to hormone receptor expression. HER-2 status was not associated with any of the image features. Finally, SUV max , SUV mean and TLG significantly differed among the three phenotype subgroups (HER2-positive, triple-negative and ER-positive/HER2-negative BCs), but MATV and heterogeneity metrics were not discriminative. Conclusion SUV parameters, MATV and textural features showed limited correlations with clinical and histopathological features. The three main BC subgroups differed in terms of SUVs and TLG but not in terms of MATV and heterogeneity. None of the PET-derived metrics offered high discriminative power.

The aim of this retrospective study was to determine if some features of baseline (18)F-FDG PET images, including volume and heterogeneity, reflect clinical, histological or immunohistochemical characteristics in patients with stage II or III breast cancer (BC). Included in the present retrospective analysis were 171 prospectively recruited patients with stage II/III BC treated consecutively at Saint-Louis hospital. Primary tumour volumes were semiautomatically delineated on pretreatment (18)F-FDG PET images. The parameters extracted included SUVmax, SUVmean, metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and heterogeneity quantified using the area under the curve of the cumulative histogram and textural features. Associations between clinical/histopathological characteristics and (18)F-FDG PET features were assessed using one-way analysis of variance. Areas under the ROC curves (AUC) were used to quantify the discriminative power of the features significantly associated with clinical/histopathological characteristics. T3 tumours (>5 cm) exhibited higher textural heterogeneity in (18)F-FDG uptake than T2 tumours (AUC <0.75), whereas there were no significant differences in SUVmax and SUVmean. Invasive ductal carcinoma showed higher SUVmax values than invasive lobular carcinoma (p = 0.008) but MATV, TLG and textural features were not discriminative. Grade 3 tumours had higher FDG uptake (AUC 0.779 for SUVmax and 0.694 for TLG), and exhibited slightly higher regional heterogeneity (AUC 0.624). Hormone receptor-negative tumours had higher SUV values than oestrogen receptor-positive (ER-positive) and progesterone receptor-positive tumours, while heterogeneity patterns showed only low-level variation according to hormone receptor expression. HER-2 status was not associated with any of the image features. Finally, SUVmax, SUVmean and TLG significantly differed among the three phenotype subgroups (HER2-positive, triple-negative and ER-positive/HER2-negative BCs), but MATV and heterogeneity metrics were not discriminative. SUV parameters, MATV and textural features showed limited correlations with clinical and histopathological features. The three main BC subgroups differed in terms of SUVs and TLG but not in terms of MATV and heterogeneity. None of the PET-derived metrics offered high discriminative power.

Aim Processes and variables measured in ecology are almost always spatially autocorrelated, potentially leading to the choice of overly complex models when performing variable selection. One way to solve this problem is to account for residual spatial autocorrelation (RSA) for each subset of variables considered and then use a classical model selection criterion such as the Akaike information criterion (AIC). However, this method can be laborious and it raises other concerns such as which spatial model to use or how to compare different spatial models. To improve the accuracy of variable selection in ecology, this study evaluates an alternative method based on a spatial cross-validation procedure. Such a procedure is usually used for model evaluation but can also provide interesting outcomes for variable selection in the presence of spatial autocorrelation. Innovation We propose to use a special case of spatial cross-validation, spatial leave-one-out (SLOO), giving a criterion equivalent to the AIC in the absence of spatial autocorrelation. SLOO only computes non-spatial models and uses a threshold distance (equal to the range of RSA) to keep each point left out spatially independent from the others. We first provide some simulations to evaluate how SLOO performs compared with AIC. We then assess the robustness of SLOO on a large-scale dataset. R software codes are provided for generalized linear models. Main conclusions The AIC was relevant for variable selection in the presence of RSA if the independent variables considered were not spatially autocorrelated. It otherwise failed because highly spatially autocorrelated variables were more often selected than others. Conversely, SLOO had similar performances whether the variables were themselves spatially autocorrelated or not. It was particularly useful when the range of RSA was small, which is a common property of spatial tools. SLOO appears to be a promising solution for selecting relevant variables from most ecological spatial datasets.

Purpose The aim of this retrospective study was to determine if some features of baseline ^sup 18^F-FDG PET images, including volume and heterogeneity, reflect clinical, histological or immunohistochemical characteristics in patients with stage II or III breast cancer (BC). Methods Included in the present retrospective analysis were 171 prospectively recruited patients with stage II/III BC treated consecutively at Saint-Louis hospital. Primary tumour volumes were semiautomatically delineated on pretreatment ^sup 18^F-FDG PET images. The parameters extracted included SUV^sub max^, SUV^sub mean^, metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and heterogeneity quantified using the area under the curve of the cumulative histogram and textural features. Associations between clinical/histopathological characteristics and ^sup 18^F-FDG PET features were assessed using one-way analysis of variance. Areas under the ROC curves (AUC) were used to quantify the discriminative power of the features significantly associated with clinical/histopathological characteristics. Results T3 tumours (>5 cm) exhibited higher textural heterogeneity in ^sup 18^F-FDG uptake than T2 tumours (AUC <0.75), whereas there were no significant differences in SUV^sub max^ and SUV^sub mean^. Invasive ductal carcinoma showed higher SUV^sub max^ values than invasive lobular carcinoma (p=0.008) but MATV, TLG and textural features were not discriminative. Grade 3 tumours had higher FDG uptake (AUC 0.779 for SUV^sub max^ and 0.694 for TLG), and exhibited slightly higher regional heterogeneity (AUC 0.624). Hormone receptor-negative tumours had higher SUV values than oestrogen receptor-positive (ER-positive) and progesterone receptor-positive tumours, while heterogeneity patterns showed only low-level variation according to hormone receptor expression. HER-2 status was not associated with any of the image features. Finally, SUV^sub max^, SUV^sub mean^ and TLG significantly differed among the three phenotype subgroups (HER2-positive, triple-negative and ER-positive/HER2-negative BCs), but MATV and heterogeneity metrics were not discriminative. Conclusion SUV parameters, MATV and textural features showed limited correlations with clinical and histopathological features. The three main BC subgroups differed in terms of SUVs and TLG but not in terms of MATV and heterogeneity. None of the PET-derived metrics offered high discriminative power.

There remains debate over whether the accretion disk survives or is entirely disrupted after the nova eruption. In our previous paper, Muraoka et al. (2024, PASJ, 76, 293) have photometrically demonstrated that the surviving accretion disk was expanded close to the L1 point during the optical plateau stage and then drastically shrank to the tidal truncation radius after the optical plateau stage ended. To approach the clarification of the physical mechanism that drives these structural changes, we have then conducted systematic analyses of the spectral evolution of the narrow emission line components in optical over 22 d following the optical peak during the 2022 nova eruption of U Scorpii (U Sco). Additionally, we present its optical spectrum in quiescence 794 d after the 2022 nova eruption. We find that the single-peaked narrow components of H\\(\\) and He II 4686 appeared almost simultaneously between roughly days 6 and 8, preceding the onset of the disk eclipses observed after day 11. This finding suggests that the nova wind near the binary system may be the primary origin of these narrow components and even remained active several days after the nova eruption with a velocity of approximately 1000 km s\\(^-1\\), likely driving the expansion of the accretion disk until the end of the optical plateau stage. While the contribution of the rotating accretion disk might be dominated by that of the nova wind in the H\\(\\) line profile, the outward surface flow from the expanded disk might also contribute to these narrow features during the optical plateau stage, making the single-peaked narrow line profiles more pronounced.